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diff --git a/reward/IP_checkpoints/grouding-dino-base/README.md b/reward/IP_checkpoints/grouding-dino-base/README.md
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+---
+license: apache-2.0
+tags:
+- vision
+inference: false
+pipeline_tag: zero-shot-object-detection
+---
+
+# Grounding DINO model (base variant)
+
+The Grounding DINO model was proposed in [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://arxiv.org/abs/2303.05499) by Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang. Grounding DINO extends a closed-set object detection model with a text encoder, enabling open-set object detection. The model achieves remarkable results, such as 52.5 AP on COCO zero-shot.
+
+
+
+ Grounding DINO overview. Taken from the original paper.
+
+## Intended uses & limitations
+
+You can use the raw model for zero-shot object detection (the task of detecting things in an image out-of-the-box without labeled data).
+
+### How to use
+
+Here's how to use the model for zero-shot object detection:
+
+```python
+import requests
+
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+
+model_id = "IDEA-Research/grounding-dino-base"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
+
+image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(image_url, stream=True).raw)
+# Check for cats and remote controls
+# VERY important: text queries need to be lowercased + end with a dot
+text = "a cat. a remote control."
+
+inputs = processor(images=image, text=text, return_tensors="pt").to(device)
+with torch.no_grad():
+ outputs = model(**inputs)
+
+results = processor.post_process_grounded_object_detection(
+ outputs,
+ inputs.input_ids,
+ box_threshold=0.4,
+ text_threshold=0.3,
+ target_sizes=[image.size[::-1]]
+)
+```
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{liu2023grounding,
+ title={Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection},
+ author={Shilong Liu and Zhaoyang Zeng and Tianhe Ren and Feng Li and Hao Zhang and Jie Yang and Chunyuan Li and Jianwei Yang and Hang Su and Jun Zhu and Lei Zhang},
+ year={2023},
+ eprint={2303.05499},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV}
+}
+```
\ No newline at end of file
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+ "use_timm_backbone": false
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+ "##．": 30517,
+ "##／": 30518,
+ "##：": 30519,
+ "##？": 30520,
+ "##～": 30521
+ }
+ }
+}
\ No newline at end of file
diff --git a/reward/IP_checkpoints/grouding-dino-base/tokenizer_config.json b/reward/IP_checkpoints/grouding-dino-base/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ed97a84add5f9b2091e756765ad3ba087a345e17
--- /dev/null
+++ b/reward/IP_checkpoints/grouding-dino-base/tokenizer_config.json
@@ -0,0 +1,56 @@
+{
+ "added_tokens_decoder": {
+ "0": {
+ "content": "[PAD]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "100": {
+ "content": "[UNK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "101": {
+ "content": "[CLS]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "102": {
+ "content": "[SEP]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "103": {
+ "content": "[MASK]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "clean_up_tokenization_spaces": true,
+ "cls_token": "[CLS]",
+ "do_lower_case": true,
+ "mask_token": "[MASK]",
+ "model_max_length": 512,
+ "pad_token": "[PAD]",
+ "processor_class": "GroundingDinoProcessor",
+ "sep_token": "[SEP]",
+ "strip_accents": null,
+ "tokenize_chinese_chars": true,
+ "tokenizer_class": "BertTokenizer",
+ "unk_token": "[UNK]"
+}
diff --git a/reward/IP_checkpoints/grouding-dino-base/vocab.txt b/reward/IP_checkpoints/grouding-dino-base/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb140275c155a9c7c5a3b3e0e77a9e839594a938
--- /dev/null
+++ b/reward/IP_checkpoints/grouding-dino-base/vocab.txt
@@ -0,0 +1,30522 @@
+[PAD]
+[unused0]
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diff --git a/reward/weights/bert-base-uncased/README.md b/reward/weights/bert-base-uncased/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..40a2aaca31dd005eb5f6ffad07b5ffed0a31d1f6
--- /dev/null
+++ b/reward/weights/bert-base-uncased/README.md
@@ -0,0 +1,251 @@
+---
+language: en
+tags:
+- exbert
+license: apache-2.0
+datasets:
+- bookcorpus
+- wikipedia
+---
+
+# BERT base model (uncased)
+
+Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://arxiv.org/abs/1810.04805) and first released in
+[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
+between english and English.
+
+Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
+the Hugging Face team.
+
+## Model description
+
+BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
+was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of
+publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
+was pretrained with two objectives:
+
+- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
+ the entire masked sentence through the model and has to predict the masked words. This is different from traditional
+ recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
+ GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
+ sentence.
+- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
+ they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
+ predict if the two sentences were following each other or not.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features
+useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard
+classifier using the features produced by the BERT model as inputs.
+
+## Model variations
+
+BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.
+Chinese and multilingual uncased and cased versions followed shortly after.
+Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.
+Other 24 smaller models are released afterward.
+
+The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github.
+
+| Model | #params | Language |
+|------------------------|--------------------------------|-------|
+| [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M | English |
+| [`bert-large-uncased`](https://huggingface.co/bert-large-uncased) | 340M | English | sub
+| [`bert-base-cased`](https://huggingface.co/bert-base-cased) | 110M | English |
+| [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M | English |
+| [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M | Chinese |
+| [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple |
+| [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English |
+| [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English |
+
+## Intended uses & limitations
+
+You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
+be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
+fine-tuned versions of a task that interests you.
+
+Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
+to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
+generation you should look at model like GPT2.
+
+### How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
+>>> unmasker("Hello I'm a [MASK] model.")
+
+[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
+ 'score': 0.1073106899857521,
+ 'token': 4827,
+ 'token_str': 'fashion'},
+ {'sequence': "[CLS] hello i'm a role model. [SEP]",
+ 'score': 0.08774490654468536,
+ 'token': 2535,
+ 'token_str': 'role'},
+ {'sequence': "[CLS] hello i'm a new model. [SEP]",
+ 'score': 0.05338378623127937,
+ 'token': 2047,
+ 'token_str': 'new'},
+ {'sequence': "[CLS] hello i'm a super model. [SEP]",
+ 'score': 0.04667217284440994,
+ 'token': 3565,
+ 'token_str': 'super'},
+ {'sequence': "[CLS] hello i'm a fine model. [SEP]",
+ 'score': 0.027095865458250046,
+ 'token': 2986,
+ 'token_str': 'fine'}]
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import BertTokenizer, BertModel
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertModel.from_pretrained("bert-base-uncased")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+and in TensorFlow:
+
+```python
+from transformers import BertTokenizer, TFBertModel
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = TFBertModel.from_pretrained("bert-base-uncased")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+### Limitations and bias
+
+Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
+predictions:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
+>>> unmasker("The man worked as a [MASK].")
+
+[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
+ 'score': 0.09747550636529922,
+ 'token': 10533,
+ 'token_str': 'carpenter'},
+ {'sequence': '[CLS] the man worked as a waiter. [SEP]',
+ 'score': 0.0523831807076931,
+ 'token': 15610,
+ 'token_str': 'waiter'},
+ {'sequence': '[CLS] the man worked as a barber. [SEP]',
+ 'score': 0.04962705448269844,
+ 'token': 13362,
+ 'token_str': 'barber'},
+ {'sequence': '[CLS] the man worked as a mechanic. [SEP]',
+ 'score': 0.03788609802722931,
+ 'token': 15893,
+ 'token_str': 'mechanic'},
+ {'sequence': '[CLS] the man worked as a salesman. [SEP]',
+ 'score': 0.037680890411138535,
+ 'token': 18968,
+ 'token_str': 'salesman'}]
+
+>>> unmasker("The woman worked as a [MASK].")
+
+[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
+ 'score': 0.21981462836265564,
+ 'token': 6821,
+ 'token_str': 'nurse'},
+ {'sequence': '[CLS] the woman worked as a waitress. [SEP]',
+ 'score': 0.1597415804862976,
+ 'token': 13877,
+ 'token_str': 'waitress'},
+ {'sequence': '[CLS] the woman worked as a maid. [SEP]',
+ 'score': 0.1154729500412941,
+ 'token': 10850,
+ 'token_str': 'maid'},
+ {'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
+ 'score': 0.037968918681144714,
+ 'token': 19215,
+ 'token_str': 'prostitute'},
+ {'sequence': '[CLS] the woman worked as a cook. [SEP]',
+ 'score': 0.03042375110089779,
+ 'token': 5660,
+ 'token_str': 'cook'}]
+```
+
+This bias will also affect all fine-tuned versions of this model.
+
+## Training data
+
+The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
+unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
+headers).
+
+## Training procedure
+
+### Preprocessing
+
+The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
+then of the form:
+
+```
+[CLS] Sentence A [SEP] Sentence B [SEP]
+```
+
+With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus, and in
+the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
+consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
+"sentences" has a combined length of less than 512 tokens.
+
+The details of the masking procedure for each sentence are the following:
+- 15% of the tokens are masked.
+- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
+- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
+- In the 10% remaining cases, the masked tokens are left as is.
+
+### Pretraining
+
+The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
+of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
+used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
+learning rate warmup for 10,000 steps and linear decay of the learning rate after.
+
+## Evaluation results
+
+When fine-tuned on downstream tasks, this model achieves the following results:
+
+Glue test results:
+
+| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
+|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
+| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-1810-04805,
+ author = {Jacob Devlin and
+ Ming{-}Wei Chang and
+ Kenton Lee and
+ Kristina Toutanova},
+ title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
+ Understanding},
+ journal = {CoRR},
+ volume = {abs/1810.04805},
+ year = {2018},
+ url = {http://arxiv.org/abs/1810.04805},
+ archivePrefix = {arXiv},
+ eprint = {1810.04805},
+ timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
+ biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
+ bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+
+ 
+
diff --git a/reward/weights/bert-base-uncased/config.json b/reward/weights/bert-base-uncased/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..45a2321a7ecfdaaf60a6c1fd7f5463994cc8907d
--- /dev/null
+++ b/reward/weights/bert-base-uncased/config.json
@@ -0,0 +1,23 @@
+{
+ "architectures": [
+ "BertForMaskedLM"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "gradient_checkpointing": false,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "layer_norm_eps": 1e-12,
+ "max_position_embeddings": 512,
+ "model_type": "bert",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "pad_token_id": 0,
+ "position_embedding_type": "absolute",
+ "transformers_version": "4.6.0.dev0",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 30522
+}
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+ "description": "CoreML Model Specification",
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+ "path": "com.apple.CoreML/model.mlmodel"
+ },
+ "D545B13F-2D5E-4CFB-BFF1-C10E9EFD70DA": {
+ "author": "com.apple.CoreML",
+ "description": "CoreML Model Weights",
+ "name": "weights",
+ "path": "com.apple.CoreML/weights"
+ }
+ },
+ "rootModelIdentifier": "9D749A46-ADA0-43CA-B5C2-8E722B91F41E"
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diff --git a/reward/weights/copy_data.sh b/reward/weights/copy_data.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4bcdb9ed129ed9a68a9677cc05239ecf32402dfb
--- /dev/null
+++ b/reward/weights/copy_data.sh
@@ -0,0 +1,25 @@
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/chenbaoyou/work/ttv/vae/benchmark/videos/ogv/209258028_267845299-Scene-220.mp4 .
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/chenbaoyou/work/ttv/vae/benchmark/videos/ogv/ . --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/chenbaoyou/work/ttv/vae/benchmark/videos/kinetics/ kinetics --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/chenbaoyou/work/ttv/vae/benchmark/videos/panda/ panda --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/wuxiaoxuan/test_sub_data test_sub_data --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_data/wuyidi/annotate/ annotate --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_data/wuyidi/actionclip-traindata/ actionclip-traindata --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/chenbaoyou/dataset/ogv
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/xubaohan/video_generation/cogvideox_bilibili/data/animate_clip_0812_all_filter_cap_aes_flow_cm_motion35_6s.json .
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp dongman_0901_11w.json s3://cv_platform/seasonyang/data/caption_data/dongman_0901_11w.json
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp dongman_0901_42w.json s3://cv_platform/seasonyang/data/caption_data/dongman_0901_42w.json
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/yinmingyu/tmp/animate_clip_0812_all_filter_cap_aes_flow_cm_motion35_3s_new.json .
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/yinmingyu/tmp/animate_clip_0812_all_filter_cap_aes_flow_cm_motion35_new.json .
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/seasonyang/data/vae_eval_data_480p/ vae_eval_data_480p --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp eval_res s3://cv_platform/seasonyang/data/eval_res --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp benchmark_sample_200.csv s3://cv_platform/seasonyang/data/list/benchmark_sample_200.csv
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/yinmingyu/tmp/benckmark_1106/720/generated_v2 generated_v2 --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/yinmingyu/tmp/benckmark_1106/720/generated_v2 generated_v2 --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/xubaohan/video_generation/anisora_benchmark/checkpoints/Mantis-8B-Idefics2/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/xubaohan/video_generation/anisora_benchmark/checkpoints/Mantis-8B-Idefics2/mantis-8b-idefics2-video-eval-24frame-quality_4096_regression mantis-8b-idefics2-video-eval-24frame-quality_4096_regression --recursive
+aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/yinmingyu/DATA/workshop/personal/benchmark/BLIP/output/Pretrain_humangt_meanvideo_trainset_wGT Pretrain_humangt_meanvideo_trainset_wGT --recursive
+aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/yinmingyu/DATA/workshop/personal/benchmark/BLIP/output/Pretrain_text_humangt_meanvideo_trainset_wGT_random_samescore Pretrain_text_humangt_meanvideo_trainset_wGT_random_samescore --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/wuxiaoxuan/train_datas train_data.json
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_platform/wuxiaoxuan/test_datas test_datas --recursive
+#aws --endpoint-url=http://jssz-boss.bilibili.co s3 cp s3://cv_data/wuyidi/benchmark_semi_results/ benchmark_semi_results --recursive
diff --git a/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/added_tokens.json b/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..92c64f2802f0f0606fb89ef69b3752af6b4cdef6
--- /dev/null
+++ b/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/added_tokens.json
@@ -0,0 +1,5 @@
+{
+ "": 32002,
+ "": 32000,
+ "": 32001
+}
diff --git a/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/chat_template.json b/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/chat_template.json
new file mode 100644
index 0000000000000000000000000000000000000000..2c965c0c44f4b2b4981fefbb0ffda0e44ab3d5ee
--- /dev/null
+++ b/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/chat_template.json
@@ -0,0 +1,3 @@
+{
+ "chat_template": "{% for message in messages %}{{message['role'].capitalize()}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '' }}{% endif %}{% endfor %}\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
+}
diff --git a/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/config.json b/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c4e2784a46d8bed7ef19fb15da4fb409e0c58124
--- /dev/null
+++ b/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/config.json
@@ -0,0 +1,241 @@
+{
+ "_name_or_path": "/DATA/bvac/personal/video_generation/benchmark/VideoScore/Mantis/mantis/Mantis-8B-Idefics2",
+ "architectures": [
+ "Idefics2ForSequenceClassification"
+ ],
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+ }
+}
diff --git a/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/generation_config.json b/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c102b8c65693d2ed693645da1d90a0f5e49cf397
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+++ b/reward/weights/mantis-8b-idefics2-video-eval-24frame-benchmark-1epoch_4096_regression/checkpoint-final/generation_config.json
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+ "▁gehör": 17421,
+ "▁▁▁▁▁▁▁▁▁▁▁": 17422,
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+ "▁Tot": 17471,
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+ "Times": 17473,
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